FedVSE: A Privacy-Preserving and Efficient Vector Search Engine for Federated Databases

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Abstract

Efficient vector search is a foundational capability of vector databases. However, most prior research overlooks its critical role in federated databases for applications like financial risk control and smart healthcare. In these privacy-sensitive scenarios, a vector search engine must not only deliver high performance but also guarantee privacy across federated databases. Current solutions, however, struggle with scalability for high-dimensional vectors, and offer limited query support. To bridge this gap, this paper introduces FedVSE, a privacy-preserving vector search engine for federated databases. FedVSE supports both KNN and hybrid queries, matching the versatility of modern vector databases. It leverages Intel SGX for hardware-enabled security and offers highly optimized query processing via indexing and pruning. Conference audiences can interact with FedVSE in real time and observe how it enables real-world services like cross-platform trajectory similarity search.